Currently many countries are struggling to rationalize water quality monitoring stations which is caused by economic demand. Though this process is essential indeed, the exact elements of the system to be optimized without a subsequent quality and accuracy loss still remain obscure. Therefore, accurate historical data on groundwater pollution is required to detect and monitor considerable environmental impacts. To collect such data appropriate sampling and assessment methodologies with an optimum spatial distribution augmented should be exploited. Thus, the configuration of water monitoring sampling points and the number of the points required are now considered as a fundamental optimization challenge. The paper offers and tests metaheuristic approaches for optimization of monitoring procedure and multi-factors assessment of water quality in “New Moscow” area. It is shown that the considered algorithms allow us to reduce the size of the training sample set, so that the number of points for monitoring water quality in the area can be halved. Moreover, reducing the dataset size improved the quality of prediction by 20%. The obtained results convincingly demonstrate that the proposed algorithms dramatically decrease the total cost of analysis without dampening the quality of monitoring and could be recommended for optimization purposes.

The aim of the work is to create a scenario modeling technique based on adaptation of metaheuristic multi-objective optimization algorithms to construct Pareto-optimal investment portfolios taking into account multiple restrictions on the company's portfolio. Investment portfolio is a tool for company's resources management. The process of portfolio construction through a combination of individual investment projects aims to optimize available resources utilization. Currently, there is no single approach to determining the optimal portfolio, since portfolio requirements depend on a particular company. Due to its simplicity in terms of implementation, approaches based on the ranking of projects according to individual technical and economic indicators are widely used. However, if a company needs to take into account several indicators, then this approach is not applicable. An effective solution to this problem is to create a technique that allows to find Pareto-optimal portfolios. In this work, the problem statement and analysis of existing solving approaches were carried out. Based on that, methodology of scenario modeling of a company's portfolio was created. The posed problem belongs to the class of NP-hard problems and for its efficient solving it is necessary to use metaheuristic algorithms. These algorithms include genetic algorithms, variable neighborhood search, bees algorithm, etc. Genetic algorithm NSGA-II is widely used for solving multi-objective optimization problems due to its high performance and ease of use. This algorithm was adapted to solve proposed problem. Based on it, a methodology was developed for the formation of Pareto-optimal investment portfolios, taking into account financial and infrastructural restrictions. Testing of the methodology was carried out on a dataset of synthetic projects simulating real investment projects. Various combinations of this projects simulated investment portfolios. The optimization objectives were maximization of net present value (NPV), maximization of cumulative oil production and minimization of total investments. In order to control the quality of the results obtained using genetic algorithm, the problem was also solved by using variable neighborhood search. However, the quality of the results obtained using genetic algorithm was higher. Analysis of the results allows us to consider the use of multi-objective optimization as a tool for forming a pool of investment portfolios, which provides flexible company management. In the proposed work, it is shown that the problem of scenario-based modeling of company's investment portfolio can be considered as a multi-objective optimization problem. Based on the existing metaheuristic algorithms, a Pareto-optimal investment portfolio search methodology has been developed. Proposed methodology can effectively solve the problem of managing company resources.